Semi-global matching (SGM) is a technique of choice for dense stereo estimation in current industrial driver-assistance systems due to its real-time processing capability and its convincing performance. In this paper we introduce iSGM as a new cost integration concept for semi-global matching. In iSGM, accumulated costs are iteratively evaluated and intermediate disparity results serve as input to generate semi-global distance maps. This novel data structure supports fast analysis of spatial disparity information and allows for reliable search space reduction in consecutive cost accumulation. As a consequence horizontal costs are stabilized which improves the robustness of the matching result. We demonstrate the superiority of this iterative integration concept against a standard configuration of semi-global matching and compare our results to current state-of-the-art methods on the KITTI Vision Benchmark Suite. © 2013 Springer-Verlag.
CITATION STYLE
Hermann, S., & Klette, R. (2013). Iterative semi-global matching for robust driver assistance systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7726 LNCS, pp. 465–478). https://doi.org/10.1007/978-3-642-37431-9_36
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